The Theory of Filtrations of Subalgebras, Standardness and Independence
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Notes on Elementary Martingale Theory 1 Conditional Expectations
. Notes on Elementary Martingale Theory by John B. Walsh 1 Conditional Expectations 1.1 Motivation Probability is a measure of ignorance. When new information decreases that ignorance, it changes our probabilities. Suppose we roll a pair of dice, but don't look immediately at the outcome. The result is there for anyone to see, but if we haven't yet looked, as far as we are concerned, the probability that a two (\snake eyes") is showing is the same as it was before we rolled the dice, 1/36. Now suppose that we happen to see that one of the two dice shows a one, but we do not see the second. We reason that we have rolled a two if|and only if|the unseen die is a one. This has probability 1/6. The extra information has changed the probability that our roll is a two from 1/36 to 1/6. It has given us a new probability, which we call a conditional probability. In general, if A and B are events, we say the conditional probability that B occurs given that A occurs is the conditional probability of B given A. This is given by the well-known formula P A B (1) P B A = f \ g; f j g P A f g providing P A > 0. (Just to keep ourselves out of trouble if we need to apply this to a set f g of probability zero, we make the convention that P B A = 0 if P A = 0.) Conditional probabilities are familiar, but that doesn't stop themf fromj g giving risef tog many of the most puzzling paradoxes in probability. -
An Application of Probability Theory in Finance: the Black-Scholes Formula
AN APPLICATION OF PROBABILITY THEORY IN FINANCE: THE BLACK-SCHOLES FORMULA EFFY FANG Abstract. In this paper, we start from the building blocks of probability the- ory, including σ-field and measurable functions, and then proceed to a formal introduction of probability theory. Later, we introduce stochastic processes, martingales and Wiener processes in particular. Lastly, we present an appli- cation - the Black-Scholes Formula, a model used to price options in financial markets. Contents 1. The Building Blocks 1 2. Probability 4 3. Martingales 7 4. Wiener Processes 8 5. The Black-Scholes Formula 9 References 14 1. The Building Blocks Consider the set Ω of all possible outcomes of a random process. When the set is small, for example, the outcomes of tossing a coin, we can analyze case by case. However, when the set gets larger, we would need to study the collections of subsets of Ω that may be taken as a suitable set of events. We define such a collection as a σ-field. Definition 1.1. A σ-field on a set Ω is a collection F of subsets of Ω which obeys the following rules or axioms: (a) Ω 2 F (b) if A 2 F, then Ac 2 F 1 S1 (c) if fAngn=1 is a sequence in F, then n=1 An 2 F The pair (Ω; F) is called a measurable space. Proposition 1.2. Let (Ω; F) be a measurable space. Then (1) ; 2 F k SK (2) if fAngn=1 is a finite sequence of F-measurable sets, then n=1 An 2 F 1 T1 (3) if fAngn=1 is a sequence of F-measurable sets, then n=1 An 2 F Date: August 28,2015. -
Mathematical Finance an Introduction in Discrete Time
Jan Kallsen Mathematical Finance An introduction in discrete time CAU zu Kiel, WS 13/14, February 10, 2014 Contents 0 Some notions from stochastics 4 0.1 Basics . .4 0.1.1 Probability spaces . .4 0.1.2 Random variables . .5 0.1.3 Conditional probabilities and expectations . .6 0.2 Absolute continuity and equivalence . .7 0.3 Conditional expectation . .8 0.3.1 σ-fields . .8 0.3.2 Conditional expectation relative to a σ-field . 10 1 Discrete stochastic calculus 16 1.1 Stochastic processes . 16 1.2 Martingales . 18 1.3 Stochastic integral . 20 1.4 Intuitive summary of some notions from this chapter . 27 2 Modelling financial markets 36 2.1 Assets and trading strategies . 36 2.2 Arbitrage . 39 2.3 Dividend payments . 41 2.4 Concrete models . 43 3 Derivative pricing and hedging 48 3.1 Contingent claims . 49 3.2 Liquidly traded derivatives . 50 3.3 Individual perspective . 54 3.4 Examples . 56 3.4.1 Forward contract . 56 3.4.2 Futures contract . 57 3.4.3 European call and put options in the binomial model . 57 3.4.4 European call and put options in the standard model . 61 3.5 American options . 63 2 CONTENTS 3 4 Incomplete markets 70 4.1 Martingale modelling . 70 4.2 Variance-optimal hedging . 72 5 Portfolio optimization 73 5.1 Maximizing expected utility of terminal wealth . 73 6 Elements of continuous-time finance 80 6.1 Continuous-time theory . 80 6.2 Black-Scholes model . 84 Chapter 0 Some notions from stochastics 0.1 Basics 0.1.1 Probability spaces This course requires a decent background in stochastics which cannot be provided here. -
4 Martingales in Discrete-Time
4 Martingales in Discrete-Time Suppose that (Ω, F, P) is a probability space. Definition 4.1. A sequence F = {Fn, n = 0, 1,...} is called a filtration if each Fn is a sub-σ-algebra of F, and Fn ⊆ Fn+1 for n = 0, 1, 2,.... In other words, a filtration is an increasing sequence of sub-σ-algebras of F. For nota- tional convenience, we define ∞ ! [ F∞ = σ Fn n=1 and note that F∞ ⊆ F. Definition 4.2. If F = {Fn, n = 0, 1,...} is a filtration, and X = {Xn, n = 0, 1,...} is a discrete-time stochastic process, then X is said to be adapted to F if Xn is Fn-measurable for each n. Note that by definition every process {Xn, n = 0, 1,...} is adapted to its natural filtration {Fn, n = 0, 1,...} where Fn = σ(X0,...,Xn). The intuition behind the idea of a filtration is as follows. At time n, all of the information about ω ∈ Ω that is known to us at time n is contained in Fn. As n increases, our knowledge of ω also increases (or, if you prefer, does not decrease) and this is captured in the fact that the filtration consists of an increasing sequence of sub-σ-algebras. Definition 4.3. A discrete-time stochastic process X = {Xn, n = 0, 1,...} is called a martingale (with respect to the filtration F = {Fn, n = 0, 1,...}) if for each n = 0, 1, 2,..., (a) X is adapted to F; that is, Xn is Fn-measurable, 1 (b) Xn is in L ; that is, E|Xn| < ∞, and (c) E(Xn+1|Fn) = Xn a.s. -
The Entropy Function for Non Polynomial Problems and Its Applications for Turing Machines
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 30 January 2020 doi:10.20944/preprints202001.0360.v1 The entropy function for non polynomial problems and its applications for turing machines. Matheus Santana Harvard Extension School [email protected] Abstract We present a general process for the halting problem, valid regardless of the time and space computational complexity of the decision problem. It can be interpreted as the maximization of entropy for the utility function of a given Shannon-Kolmogorov-Bernoulli process. Applications to non-polynomials prob- lems are given. The new interpretation of information rate proposed in this work is a method that models the solution space boundaries of any decision problem (and non polynomial problems in general) as a communication channel by means of Information Theory. We described a sort method that order objects using the intrinsic information content distribution for the elements of a constrained solution space - modeled as messages transmitted through any communication systems. The limits of the search space are defined by the Kolmogorov-Chaitin complexity of the sequences encoded as Shannon-Bernoulli strings. We conclude with a discussion about the implications for general decision problems in Turing machines. Keywords: Computational Complexity, Information Theory, Machine Learning, Computational Statistics, Kolmogorov-Chaitin Complexity; Kelly criterion 1. Introduction Consider a Shanon-Bernoulli process P defined as a sequence of independent binary random variables X1, X2, X3 . Xn. For each element the value can be Preprint submitted to Journal of Information and Computation January 29, 2020 © 2020 by the author(s). Distributed under a Creative Commons CC BY license. -
A Structural Approach to Solving the 6Th Hilbert Problem Udc 519.21
Teor Imovr.taMatem.Statist. Theor. Probability and Math. Statist. Vip. 71, 2004 No. 71, 2005, Pages 165–179 S 0094-9000(05)00656-3 Article electronically published on December 30, 2005 A STRUCTURAL APPROACH TO SOLVING THE 6TH HILBERT PROBLEM UDC 519.21 YU. I. PETUNIN AND D. A. KLYUSHIN Abstract. The paper deals with an approach to solving the 6th Hilbert problem based on interpreting the field of random events as a partially ordered set endowed with a natural order of random events obtained by formalization and modification of the frequency definition of probability. It is shown that the field of events forms an atomic generated, complete, and completely distributive Boolean algebra. The probability distribution of the field of events generated by random variables is studied. It is proved that the probability distribution generated by random variables is not a measure but only a finitely additive function of events in the case of continuous random variables (both rational- and real-valued). Introduction In 1900 in his lecture [1] David Hilbert formulated the problem of axiomatization of probability theory. Here is the corresponding quote from his lecture: “The investigations on the foundations of geometry suggest the problem: To treat in the same manner, by means of axioms, those physical sciences in which mathematics plays an important part; in the first rank are the theory of probabilities and mechanics. As to the axioms of the theory of probabilities, it seems to me desirable that their logical investigation should be accompanied by a rigorous and satisfactory development of the method of mean values in mathematical physics, and in particular in the kinetic theory of gases.” As is well known [2], there is no generally accepted solution of this problem so far. -
MTH 664 Lectures 24 - 27
MTH 664 0 MTH 664 Lectures 24 - 27 Yevgeniy Kovchegov Oregon State University MTH 664 1 Topics: • Martingales. • Filtration. Stopping times. • Probability harmonic functions. • Optional Stopping Theorem. • Martingale Convergence Theorem. MTH 664 2 Conditional expectation. Consider a probability space (Ω, F,P ) and a random variable X ∈ F. Let G ⊆ F be a smaller σ-algebra. Definition. Conditional expectation E[X|G] is a unique func- tion from Ω to R satisfying: 1. E[X|G] is G-measurable 2. R E[X|G] dP (ω) = R X dP (ω) for all A ∈ G A A The existence and uniqueness of E[X|G] comes from the Radon-Nikodym theorem. Lemma. If X ∈ G, Y (ω) ∈ L1(Ω,P ), and X(ω) · Y (ω) ∈ L1(Ω,P ), then E[X · Y |G] = X · E[Y |G] MTH 664 3 Conditional expectation. Consider a probability space (Ω, F,P ) and a random variable X ∈ F. Lemma. If G ⊆ F, then EE[X|G] = E[X] Let G1 ⊆ G2 ⊆ F be smaller sub-σ-algebras. Lemma. E E[X|G2] | G1 = E[X|G1] Proof. For any A ∈ G1 ⊆ G2, Z Z E E[X|G2] | G1 (ω) dP (ω) = E[X|G2](ω) dP (ω) A A Z Z = X(ω) dP (ω) = E[X|G1](ω) dP (ω) A A MTH 664 4 Filtration. Definition. Consider an arbitrary linear ordered set T :A sequence of sub-σ-algebras {Ft}t∈T of F is said to be a filtration if Fs ⊆ Ft a.s. ∀s < t ∈ T Example. Consider a sequence of random variables X1,X2,.. -
An Enlargement of Filtration Formula with Applications to Multiple Non
Finance and Stochastics manuscript No. (will be inserted by the editor) An enlargement of filtration formula with applications to multiple non-ordered default times Monique Jeanblanc · Libo Li · Shiqi Song Received: date / Accepted: date Abstract In this work, for a reference filtration F, we develop a method for computing the semimartingale decomposition of F-martingales in a specific type of enlargement of F. As an application, we study the progressive en- largement of F with a sequence of non-ordered default times and we show how to deduce results concerning the first-to-default, k-th-to-default, k-out-of- n-to-default or the all-to-default events. In particular, using this method, we compute explicitly the semimartingale decomposition of F-martingales under the absolute continuity condition of Jacod. Keywords Enlargement of filtration · Non-ordered default times · Reduced form credit risk modeling · Semimartingale decomposition JEL classification G14 Mathematics Subject Classification (2010) 60G07 · 91G40 This research benefited from the support of the Chaire Risque de Cr´edit(F´ed´erationBan- caire Fran¸caise)and Labex ANR 11-LABX-0019. Monique Jeanblanc Univ Evry, LaMME, UMR CNRS 8071, Universit´eParis Saclay, 23 boulevard de France, 91037 Evry. E-mail: [email protected] Libo Li ( ) School of Mathematics and Statistics, University of New South Wales, NSW, Australia. E-mail: [email protected] Shiqi Song Univ Evry, LaMME, UMR CNRS 8071, Universit´eParis Saclay, 23 boulevard de France, 91037 Evry. E-mail: [email protected] 2 M. Jeanblanc, L. Li and S. Song 1 Introduction This paper is motivated by the needs to model multiple non-ordered default times in credit risk and to perform explicit computations in the pricing of credit instruments. -
The Comparison Isomorphisms Ccris
The Comparison Isomorphisms Ccris Fabrizio Andreatta July 9, 2009 Contents 1 Introduction 2 2 The crystalline conjecture with coe±cients 3 3 The complex case. The strategy in the p-adic setting 4 4 The p-adic case; Faltings's topology 5 5 Sheaves of periods 7 i 6 The computation of R v¤Bcris 13 7 The proof of the comparison isomorphism 16 1 1 Introduction Let p > 0 denote a prime integer and let k be a perfect ¯eld of characteristic p. Put OK := W (k) and let K be its fraction ¯eld. Denote by K a ¯xed algebraic closure of K and set GK := Gal(K=K). We write Bcris for the crystalline period ring de¯ned by J.-M. Fontaine in [Fo]. Recall that Bcris is a topological ring, endowed with a continuous action of GK , an ² exhaustive decreasing ¯ltration Fil Bcris and a Frobenius operator '. Following the paper [AI2], these notes aim at presenting a new proof of the so called crys- talline conjecture formulated by J.-M. Fontaine in [Fo]. Let X be a smooth proper scheme, geometrically irreducible, of relative dimension d over OK : Conjecture 1.1 ([Fo]). For every i ¸ 0 there is a canonical and functorial isomorphism com- muting with all the additional structures (namely, ¯ltrations, GK {actions and Frobenii) i et » i H (XK ; Qp) Qp Bcris = Hcris(Xk=OK ) OK Bcris: i » It follows from a classical theorem for crystalline cohomology, see [BO], that Hcris(Xk=OK ) = i HdR(X=OK ). The latter denotes the hypercohomology of the de Rham complex 0 ¡! OX ¡! 1 ¡! 2 ¡! ¢ ¢ ¢ d ¡! 0. -
E1-Degeneration of the Irregular Hodge Filtration (With an Appendix by Morihiko Saito)
E1-DEGENERATION OF THE IRREGULAR HODGE FILTRATION (WITH AN APPENDIX BY MORIHIKO SAITO) HÉLÈNE ESNAULT, CLAUDE SABBAH, AND JENG-DAW YU Abstract. For a regular function f on a smooth complex quasi-projective va- riety, J.-D. Yu introduced in [Yu14] a filtration (the irregular Hodge filtration) on the de Rham complex with twisted differential d+df, extending a definition of Deligne in the case of curves. In this article, we show the degeneration at E1 of the spectral sequence attached to the irregular Hodge filtration, by using the method of [Sab10]. We also make explicit the relation with a complex introduced by M. Kontsevich and give details on his proof of the correspond- ing E1-degeneration, by reduction to characteristic p, when the pole divisor of the function is reduced with normal crossings. In Appendix E, M. Saito gives a different proof of the latter statement with a possibly non reduced normal crossing pole divisor. Contents Introduction 2 1. The irregular Hodge filtration in the normal crossing case 3 1.1. Setup and notation 3 1.2. The irregular Hodge filtration 5 1.3. The Kontsevich complex 6 1.4. Comparison of the filtered twisted meromorphic de Rham complex and the filtered Kontsevich complex 7 1.5. Relation between Theorem 1.2.2 for α = 0 and Theorem 1.3.2 9 1.6. Deligne’s filtration: the D-module approach 11 1.7. Comparison of Yu’s filtration and Deligne’s filtration on the twisted de Rham complex 16 2. Preliminaries to a generalization of Deligne’s filtration 18 2.1. -
LECTURES 3 and 4: MARTINGALES 1. Introduction in an Arbitrage–Free
LECTURES 3 AND 4: MARTINGALES 1. Introduction In an arbitrage–free market, the share price of any traded asset at time t = 0 is the expected value, under an equilibrium measure, of its discounted price at market termination t = T . We shall see that the discounted share price at any time t ≤ T may also be computed by an expectation; however, this expectation is a conditional expectation, given the information about the market scenario that is revealed up to time t. Thus, the discounted share price process, as a (random) function of time, has the property that its value at any time t is the conditional expectation of the value at a future time given information available at t.A stochastic process with this property is called a martingale. The theory of martingales (initiated by Joseph Doob, following earlier work of Paul Levy´ ) is one of the central themes of modern probability. In discrete-time finance, the importance of martingales stems largely from the Optional Stopping Formula, which (a) places fundamental constraints on (discounted) share price processes, and (b) provides a computational tool of considerable utility. 2. Filtrations of Probability Spaces Filtrations. In a multiperiod market, information about the market scenario is revealed in stages. Some events may be completely determined by the end of the first trading period, others by the end of the second, and others not until the termination of all trading. This suggests the following classification of events: for each t ≤ T , (1) Ft = {all events determined in the first t trading periods}. The finite sequence (Ft)0≤t≤T is a filtration of the space Ω of market scenarios. -
The Ergodic Theorem
The Ergodic Theorem 1 Introduction Ergodic theory is a branch of mathematics which uses measure theory to study the long term be- haviour of dynamic systems. The central object of consideration is known as a measure-preserving system, a type of dynamic system where the evolution of the system preserves a measure. Definition 1: Let (X; M; µ) be a finite measure space and let T map X to itself. We say that T is a measure-preserving transformation if for every A 2 M, we have µ(A) = µ(T −1(A)). The quadruple (X; M; µ, T ) is called a measure-preserving system (m.p.s.). Measure-preserving systems arise in a variety of contexts, such as probability theory, informa- tion theory, and of course in the study of dynamical systems. However, ergodic theory originated from statistical mechanics. In this setting, T represents the evolution of the system through time. Given a measurable function f : X ! R, the series of values f(x); f(T x); f(T 2x)::: are the values of a physical observable at certain time intervals. Of importance in statistical mechanics is the long-term average of these observables: N−1 1 X f (x) = f(T kx) N N k=0 The Ergodic Theorem (also known as the Pointwise or Birkhoff Ergodic Theorem) is central to the study of averages such as fN in the limit as N ! 1. In this paper we aim to prove the theorem, and then discuss a few of its applications. Before we can state the theorem, we need another definition.